{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "2e18e822",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebe36c03",
   "metadata": {},
   "source": [
    "### 数据分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2933f935",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8d2968b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_train_data = pd.read_csv(r\"D:\\SparrowRecSys-master\\src\\main\\resources\\webroot\\sampledata\\trainingSamples.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "74184c96",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>userId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>label</th>\n",
       "      <th>releaseYear</th>\n",
       "      <th>movieGenre1</th>\n",
       "      <th>movieGenre2</th>\n",
       "      <th>movieGenre3</th>\n",
       "      <th>movieRatingCount</th>\n",
       "      <th>...</th>\n",
       "      <th>userRatingCount</th>\n",
       "      <th>userAvgReleaseYear</th>\n",
       "      <th>userReleaseYearStddev</th>\n",
       "      <th>userAvgRating</th>\n",
       "      <th>userRatingStddev</th>\n",
       "      <th>userGenre1</th>\n",
       "      <th>userGenre2</th>\n",
       "      <th>userGenre3</th>\n",
       "      <th>userGenre4</th>\n",
       "      <th>userGenre5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15555</td>\n",
       "      <td>3.0</td>\n",
       "      <td>900953740</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Animation</td>\n",
       "      <td>Children</td>\n",
       "      <td>10759</td>\n",
       "      <td>...</td>\n",
       "      <td>92</td>\n",
       "      <td>1992</td>\n",
       "      <td>8.98</td>\n",
       "      <td>3.86</td>\n",
       "      <td>0.74</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>Action</td>\n",
       "      <td>Crime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>25912</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1111631768</td>\n",
       "      <td>1</td>\n",
       "      <td>1995</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Animation</td>\n",
       "      <td>Children</td>\n",
       "      <td>10759</td>\n",
       "      <td>...</td>\n",
       "      <td>21</td>\n",
       "      <td>1988</td>\n",
       "      <td>14.09</td>\n",
       "      <td>3.48</td>\n",
       "      <td>1.28</td>\n",
       "      <td>Action</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Romance</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>29912</td>\n",
       "      <td>3.0</td>\n",
       "      <td>866820360</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Animation</td>\n",
       "      <td>Children</td>\n",
       "      <td>10759</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.50</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>17686</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1195555011</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>Action</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>6330</td>\n",
       "      <td>...</td>\n",
       "      <td>35</td>\n",
       "      <td>1992</td>\n",
       "      <td>8.35</td>\n",
       "      <td>2.97</td>\n",
       "      <td>1.48</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Action</td>\n",
       "      <td>Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>104</td>\n",
       "      <td>20158</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1155357691</td>\n",
       "      <td>1</td>\n",
       "      <td>1996</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3954</td>\n",
       "      <td>...</td>\n",
       "      <td>81</td>\n",
       "      <td>1991</td>\n",
       "      <td>8.70</td>\n",
       "      <td>3.60</td>\n",
       "      <td>0.72</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Action</td>\n",
       "      <td>Crime</td>\n",
       "      <td>Adventure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>105</td>\n",
       "      <td>21249</td>\n",
       "      <td>2.0</td>\n",
       "      <td>939466196</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2087</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>1985</td>\n",
       "      <td>17.61</td>\n",
       "      <td>4.19</td>\n",
       "      <td>0.76</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Romance</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>Crime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>105</td>\n",
       "      <td>26439</td>\n",
       "      <td>4.0</td>\n",
       "      <td>896886228</td>\n",
       "      <td>1</td>\n",
       "      <td>1995</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2087</td>\n",
       "      <td>...</td>\n",
       "      <td>28</td>\n",
       "      <td>1983</td>\n",
       "      <td>17.62</td>\n",
       "      <td>4.43</td>\n",
       "      <td>0.96</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11</td>\n",
       "      <td>14899</td>\n",
       "      <td>3.0</td>\n",
       "      <td>864503271</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "      <td>3846</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>1992</td>\n",
       "      <td>7.27</td>\n",
       "      <td>4.12</td>\n",
       "      <td>0.72</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>Crime</td>\n",
       "      <td>Action</td>\n",
       "      <td>Adventure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>110</td>\n",
       "      <td>12847</td>\n",
       "      <td>3.0</td>\n",
       "      <td>834168070</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>Action</td>\n",
       "      <td>Drama</td>\n",
       "      <td>War</td>\n",
       "      <td>11637</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>1993</td>\n",
       "      <td>2.03</td>\n",
       "      <td>3.77</td>\n",
       "      <td>0.73</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>Children</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>110</td>\n",
       "      <td>13772</td>\n",
       "      <td>5.0</td>\n",
       "      <td>907688739</td>\n",
       "      <td>1</td>\n",
       "      <td>1995</td>\n",
       "      <td>Action</td>\n",
       "      <td>Drama</td>\n",
       "      <td>War</td>\n",
       "      <td>11637</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>1993</td>\n",
       "      <td>1.49</td>\n",
       "      <td>4.60</td>\n",
       "      <td>0.52</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Crime</td>\n",
       "      <td>Romance</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId  userId  rating   timestamp  label  releaseYear movieGenre1  \\\n",
       "0        1   15555     3.0   900953740      0         1995   Adventure   \n",
       "1        1   25912     3.5  1111631768      1         1995   Adventure   \n",
       "2        1   29912     3.0   866820360      0         1995   Adventure   \n",
       "3       10   17686     0.5  1195555011      0         1995      Action   \n",
       "4      104   20158     4.0  1155357691      1         1996      Comedy   \n",
       "5      105   21249     2.0   939466196      0         1995       Drama   \n",
       "6      105   26439     4.0   896886228      1         1995       Drama   \n",
       "7       11   14899     3.0   864503271      0         1995      Comedy   \n",
       "8      110   12847     3.0   834168070      0         1995      Action   \n",
       "9      110   13772     5.0   907688739      1         1995      Action   \n",
       "\n",
       "  movieGenre2 movieGenre3  movieRatingCount  ...  userRatingCount  \\\n",
       "0   Animation    Children             10759  ...               92   \n",
       "1   Animation    Children             10759  ...               21   \n",
       "2   Animation    Children             10759  ...                4   \n",
       "3   Adventure    Thriller              6330  ...               35   \n",
       "4         NaN         NaN              3954  ...               81   \n",
       "5     Romance         NaN              2087  ...               48   \n",
       "6     Romance         NaN              2087  ...               28   \n",
       "7       Drama     Romance              3846  ...               16   \n",
       "8       Drama         War             11637  ...               13   \n",
       "9       Drama         War             11637  ...               10   \n",
       "\n",
       "   userAvgReleaseYear  userReleaseYearStddev  userAvgRating  userRatingStddev  \\\n",
       "0                1992                   8.98           3.86              0.74   \n",
       "1                1988                  14.09           3.48              1.28   \n",
       "2                1995                   0.50           3.00              0.00   \n",
       "3                1992                   8.35           2.97              1.48   \n",
       "4                1991                   8.70           3.60              0.72   \n",
       "5                1985                  17.61           4.19              0.76   \n",
       "6                1983                  17.62           4.43              0.96   \n",
       "7                1992                   7.27           4.12              0.72   \n",
       "8                1993                   2.03           3.77              0.73   \n",
       "9                1993                   1.49           4.60              0.52   \n",
       "\n",
       "   userGenre1  userGenre2  userGenre3  userGenre4  userGenre5  \n",
       "0       Drama      Comedy    Thriller      Action       Crime  \n",
       "1      Action      Comedy     Romance   Adventure    Thriller  \n",
       "2         NaN         NaN         NaN         NaN         NaN  \n",
       "3      Comedy       Drama   Adventure      Action    Thriller  \n",
       "4    Thriller       Drama      Action       Crime   Adventure  \n",
       "5       Drama      Comedy     Romance    Thriller       Crime  \n",
       "6       Drama     Romance    Thriller      Comedy      Action  \n",
       "7       Drama    Thriller       Crime      Action   Adventure  \n",
       "8   Adventure    Thriller    Children       Drama      Comedy  \n",
       "9       Drama       Crime     Romance    Thriller      Horror  \n",
       "\n",
       "[10 rows x 27 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_train_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ef72ebc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50012"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_train_data['label'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d8a8716",
   "metadata": {},
   "source": [
    "### 导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "f1466a36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training samples path, change to your local path\n",
    "training_samples_file_path = tf.keras.utils.get_file(\"trainingSamples.csv\",\n",
    "                                                     \"data\\smalldata\\trainingSamples.csv\"\n",
    "                                                    )\n",
    "# Test samples path, change to your local path\n",
    "test_samples_file_path = tf.keras.utils.get_file(\"testSamples.csv\",\n",
    "                                                 \"data\\smalldata\\testSamples.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "51c877dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "selectNames = ['label','userId','movieId','userGenre1','releaseYear','userAvgRating','userRatingStddev']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "ca21a89a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load sample as tf dataset\n",
    "def get_dataset(file_path):\n",
    "    # https://blog.csdn.net/Geek_/article/details/123493251\n",
    "    dataset = tf.data.experimental.make_csv_dataset(\n",
    "        file_path,\n",
    "        batch_size=32,\n",
    "        select_columns=selectNames,\n",
    "        label_name='label', #规定了数据集中的标签字段\n",
    "        na_value=\"0\",\n",
    "        num_epochs=1,\n",
    "        ignore_errors=True)\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "d88c51c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_test_dataset(file_path):\n",
    "    dataset = tf.data.experimental.make_csv_dataset(\n",
    "        file_path,\n",
    "        batch_size=32,\n",
    "        label_name='label', #规定了数据集中的标签字段\n",
    "        na_value=\"0\",\n",
    "        num_epochs=1,\n",
    "        shuffle=False,\n",
    "        ignore_errors=True)\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "0c8acc8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = get_dataset(training_samples_file_path)\n",
    "test_dataset = get_dataset(test_samples_file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de71d625",
   "metadata": {},
   "source": [
    "### 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "f870fe42",
   "metadata": {},
   "outputs": [],
   "source": [
    "genre_vocab = ['Film-Noir', 'Action', 'Adventure', 'Horror', 'Romance', 'War', 'Comedy', 'Western', 'Documentary',\n",
    "               'Sci-Fi', 'Drama', 'Thriller',\n",
    "               'Crime', 'Fantasy', 'Animation', 'IMAX', 'Mystery', 'Children', 'Musical']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "4eec8e96",
   "metadata": {},
   "outputs": [],
   "source": [
    "# movie id embedding feature\n",
    "# categorical_column_with_identity 输入是数值型离散值 输出类型不是Dense，是categorical\n",
    "movie_col = tf.feature_column.categorical_column_with_hash_bucket(key='movieId', hash_bucket_size=3000,dtype=tf.dtypes.int32)\n",
    "# embedding_column输出类型是Dense\n",
    "movie_emb_col = tf.feature_column.embedding_column(movie_col, 10)\n",
    "# numeric_column  数值列，不做任何处理，直接进行格式转换\n",
    "# movie_rating = tf.feature_column.numeric_column(key=\"rating\") # rating不能做为输入特征，因为label是根据rating>3.5区分的\n",
    "def normalization(x): # MinMax归一化\n",
    "    return (x-1926)/72\n",
    "# 可能需要对releaseYear进行转换,或者不使用numeric_column，使用Bucketized column，将年份按照时间区间离散为One-hot类型\n",
    "movie_released_year = tf.feature_column.numeric_column(key=\"releaseYear\")#normalizer_fn=normalization\n",
    "# movieGenre1 = tf.feature_column.categorical_column_with_vocabulary_list(key=\"movieGenre1\",vocabulary_list=genre_vocab,dtype=tf.string, default_value=-1)\n",
    "# movie_emb_genre1 = tf.feature_column.embedding_column(movieGenre1, 10)\n",
    "# movieGenre2 = tf.feature_column.categorical_column_with_vocabulary_list(key=\"movieGenre2\",vocabulary_list=genre_vocab,dtype=tf.string, default_value=-1)\n",
    "# movie_emb_genre2 = tf.feature_column.embedding_column(movieGenre2, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "330439c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# user id embedding feature\n",
    "user_col = tf.feature_column.categorical_column_with_hash_bucket(key='userId',hash_bucket_size=600,dtype=tf.dtypes.int32)\n",
    "# user_col = tf.feature_column.categorical_column_with_identity(key='userId', num_buckets=30001)\n",
    "user_emb_col = tf.feature_column.embedding_column(user_col, 5)\n",
    "user_avg_rating = tf.feature_column.numeric_column(key=\"userAvgRating\")\n",
    "user_rating_stddev = tf.feature_column.numeric_column(key=\"userRatingStddev\")\n",
    "# 将用户喜欢的电影类型转化为One-hot类型\n",
    "userGenre1 = tf.feature_column.categorical_column_with_vocabulary_list(key=\"userGenre1\",vocabulary_list=genre_vocab,dtype=tf.string, default_value=-1)\n",
    "user_emb_genre1 = tf.feature_column.embedding_column(userGenre1, 5)\n",
    "# userGenre2 = tf.feature_column.categorical_column_with_vocabulary_list(key=\"userGenre2\",vocabulary_list=genre_vocab,dtype=tf.string, default_value=-1)\n",
    "# user_emb_genre2 = tf.feature_column.embedding_column(userGenre2, 10)\n",
    "# userGenre3 = tf.feature_column.categorical_column_with_vocabulary_list(key=\"userGenre3\",vocabulary_list=genre_vocab,dtype=tf.string, default_value=-1)\n",
    "# user_emb_genre3 = tf.feature_column.embedding_column(userGenre3, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19fc2366",
   "metadata": {},
   "source": [
    "### 模型结构"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ba64e23",
   "metadata": {},
   "source": [
    "#### 模型输入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "1faac502",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = {\n",
    "    # 输入层\n",
    "    'releaseYear': tf.keras.layers.Input(name='releaseYear', shape=(1,), dtype='int32'),\n",
    "    'movieId': tf.keras.layers.Input(name='movieId', shape=(), dtype='int32'),\n",
    "#     'movieGenre1': tf.keras.layers.Input(name='movieGenre1', shape=(), dtype='string'),\n",
    "#     'movieGenre2': tf.keras.layers.Input(name='movieGenre2', shape=(), dtype='string'),\n",
    "    'userId': tf.keras.layers.Input(name='userId', shape=(), dtype='int32'),\n",
    "    'userAvgRating': tf.keras.layers.Input(name='userAvgRating', shape=(), dtype='float32'),\n",
    "    'userRatingStddev': tf.keras.layers.Input(name='userRatingStddev', shape=(), dtype='float32'),\n",
    "    'userGenre1': tf.keras.layers.Input(name='userGenre1', shape=(), dtype='string'),\n",
    "#     'userGenre2': tf.keras.layers.Input(name='userGenre2', shape=(), dtype='string'),\n",
    "#     'userGenre3': tf.keras.layers.Input(name='userGenre3', shape=(), dtype='string')\n",
    "}\n",
    "userInputs = {\n",
    "    'userId': tf.keras.layers.Input(name='userId', shape=(), dtype='int32'),\n",
    "    'userAvgRating': tf.keras.layers.Input(name='userAvgRating', shape=(), dtype='float32'),\n",
    "    'userRatingStddev': tf.keras.layers.Input(name='userRatingStddev', shape=(), dtype='float32'),\n",
    "    'userGenre1': tf.keras.layers.Input(name='userGenre1', shape=(), dtype='string')\n",
    "}\n",
    "itemInputs = {\n",
    "    'releaseYear': tf.keras.layers.Input(name='releaseYear', shape=(1,), dtype='int32'),\n",
    "    'movieId': tf.keras.layers.Input(name='movieId', shape=(), dtype='int32'),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "f4253868",
   "metadata": {},
   "outputs": [],
   "source": [
    "# neural cf model arch two. only embedding in each tower, then MLP as the interaction layers\n",
    "# feature_inputs = inputs\n",
    "def neural_cf_model_1(feature_all_inputs, userInputs, itemInputs, item_feature_columns, user_feature_columns, hidden_units):\n",
    "    # 特征列接入层，用于接收一个特征列列表【item_feature_columns】并产生一个密集连接层。\n",
    "    #物品侧特征层\n",
    "    item_tower = tf.keras.layers.DenseFeatures(item_feature_columns,name='item')(itemInputs)\n",
    "    #用户侧特征层\n",
    "    user_tower = tf.keras.layers.DenseFeatures(user_feature_columns,name='user')(userInputs)\n",
    "    # 连接层以及后续的多层神经网络。将多个张量在某个维度上拼接。\n",
    "    interact_layer = tf.keras.layers.concatenate([item_tower, user_tower])\n",
    "    for num_nodes in hidden_units:\n",
    "        # 密集连接层。参数个数 = 输入层特征数× 输出层特征数(weight)＋ 输出层特征数(bias)\n",
    "        interact_layer = tf.keras.layers.Dense(num_nodes, activation='relu')(interact_layer)\n",
    "    output_layer = tf.keras.layers.Dense(1, activation='sigmoid')(interact_layer)\n",
    "    #定义模型结构\n",
    "    neural_cf_model = tf.keras.Model([itemInputs,userInputs], output_layer)\n",
    "    return neural_cf_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e67df68",
   "metadata": {},
   "source": [
    "![](https://pic1.zhimg.com/80/v2-3c3b487d11e931f6db5db863dba23e38_1440w.webp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "fca90347",
   "metadata": {},
   "outputs": [],
   "source": [
    "# neural cf model architecture\n",
    "model = neural_cf_model_1(inputs,userInputs,itemInputs, [movie_emb_col,movie_released_year], [user_emb_col,user_avg_rating,user_rating_stddev,user_emb_genre1], [10, 10])\n",
    "\n",
    "# compile the model, set loss function, optimizer and evaluation metrics\n",
    "model.compile(\n",
    "    loss='binary_crossentropy',\n",
    "    optimizer='adam',\n",
    "    metrics=['accuracy', tf.keras.metrics.AUC(curve='ROC'), tf.keras.metrics.AUC(curve='PR')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "33cb64ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data,train_label = next(iter(train_dataset))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf6c43fa",
   "metadata": {},
   "source": [
    "### 模型训练和验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "2c08eaf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.random.set_seed(0)\n",
    "# random.seed(0)\n",
    "np.random.seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "8a88016e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "2776/2776 [==============================] - 21s 5ms/step - loss: 2.4940 - accuracy: 0.5695 - auc_28: 0.5838 - auc_29: 0.6270\n",
      "Epoch 2/10\n",
      "2776/2776 [==============================] - 14s 5ms/step - loss: 0.7744 - accuracy: 0.6178 - auc_28: 0.6541 - auc_29: 0.6958\n",
      "Epoch 3/10\n",
      "2776/2776 [==============================] - 14s 5ms/step - loss: 0.7110 - accuracy: 0.6434 - auc_28: 0.6880 - auc_29: 0.7243\n",
      "Epoch 4/10\n",
      "2776/2776 [==============================] - 15s 5ms/step - loss: 0.7021 - accuracy: 0.6468 - auc_28: 0.6934 - auc_29: 0.7298\n",
      "Epoch 5/10\n",
      "2776/2776 [==============================] - 15s 5ms/step - loss: 0.7037 - accuracy: 0.6515 - auc_28: 0.6988 - auc_29: 0.7315\n",
      "Epoch 6/10\n",
      "2776/2776 [==============================] - 15s 5ms/step - loss: 0.6797 - accuracy: 0.6591 - auc_28: 0.7091 - auc_29: 0.7417\n",
      "Epoch 7/10\n",
      "2776/2776 [==============================] - 15s 5ms/step - loss: 0.7120 - accuracy: 0.6523 - auc_28: 0.7009 - auc_29: 0.7336\n",
      "Epoch 8/10\n",
      "2776/2776 [==============================] - 14s 5ms/step - loss: 0.6867 - accuracy: 0.6590 - auc_28: 0.7101 - auc_29: 0.7415\n",
      "Epoch 9/10\n",
      "2776/2776 [==============================] - 14s 5ms/step - loss: 0.6934 - accuracy: 0.6581 - auc_28: 0.7091 - auc_29: 0.7419\n",
      "Epoch 10/10\n",
      "2776/2776 [==============================] - 14s 5ms/step - loss: 0.6945 - accuracy: 0.6602 - auc_28: 0.7112 - auc_29: 0.7423\n"
     ]
    }
   ],
   "source": [
    "# train the model\n",
    "# 在最初的数据集获取处定义了label\n",
    "# https://blog.csdn.net/m0_47256162/article/details/122309758\n",
    "# train_data, train_label = next(iter(train_dataset))\n",
    "# history = model.fit(train_data, train_label,batch_size=32,validation_split=0.2, epochs=8)\n",
    "history = model.fit(train_dataset,epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "6efafc20",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2776/2776 [==============================] - 10s 4ms/step - loss: 0.5658 - accuracy: 0.7157 - auc_28: 0.7814 - auc_29: 0.8071\n",
      "\n",
      "\n",
      "Test Loss 0.5657614469528198, Test Accuracy 0.7156832814216614, Test ROC AUC 0.7814390659332275, Test PR AUC 0.8070570230484009\n"
     ]
    }
   ],
   "source": [
    "# evaluate the model\n",
    "# test_data, test_label = next(iter(test_dataset))\n",
    "test_loss, test_accuracy, test_roc_auc, test_pr_auc = model.evaluate(test_dataset)\n",
    "print('\\n\\nTest Loss {}, Test Accuracy {}, Test ROC AUC {}, Test PR AUC {}'.format(test_loss, test_accuracy,\n",
    "                                                                                   test_roc_auc, test_pr_auc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "b1d9a38c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "keys = list(history.history.keys())\n",
    "plt.figure(figsize=(16, 6))\n",
    "acc = history.history['accuracy']\n",
    "loss = history.history['loss']\n",
    "val_accuracy = history.history['val_accuracy']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "ROC = history.history[keys[2]]\n",
    "PR = history.history[keys[3]]\n",
    "Vali_roc = history.history[keys[-2]]\n",
    "Vali_pr = history.history[keys[-1]]\n",
    "\n",
    "epochs = range(0, len(acc))\n",
    "plt.subplot(1,3,1)\n",
    "plt.title('Accuracy and Loss')\n",
    "plt.plot(epochs, acc, 'ro:', label='Training acc')\n",
    "plt.plot(epochs, loss, 'r.-', label='Training loss')\n",
    "plt.plot(epochs, val_accuracy, 'go:', label='Validation acc')\n",
    "plt.plot(epochs, val_loss, 'g.-', label='Validation loss')\n",
    "plt.xticks(ticks=epochs)\n",
    "plt.yticks(ticks=np.arange(min(loss),max(loss),0.1))\n",
    "plt.legend()\n",
    "plt.subplot(1,3,2)\n",
    "plt.title('ROC and PR')\n",
    "plt.plot(epochs, ROC, 'ro:', label='ROC')\n",
    "plt.plot(epochs, PR, 'r.-', label='PR')\n",
    "plt.plot(epochs, Vali_roc, 'go:', label='V_ROC')\n",
    "plt.plot(epochs, Vali_pr, 'g.-', label='V_PR')\n",
    "plt.xticks(ticks=epochs)\n",
    "plt.yticks(ticks=np.arange(0.3,1,0.05))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "23796e19",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "UnimplementedError",
     "evalue": "Graph execution error:\n\nDetected at node 'model_13/Cast_2' defined at (most recent call last):\n    File \"F:\\anaconda3\\envs\\tf\\lib\\runpy.py\", line 193, in _run_module_as_main\n      \"__main__\", mod_spec)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\runpy.py\", line 85, in _run_code\n      exec(code, run_globals)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\ipykernel_launcher.py\", line 17, in <module>\n      app.launch_new_instance()\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\traitlets\\config\\application.py\", line 992, in launch_instance\n      app.start()\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 712, in start\n      self.io_loop.start()\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 215, in start\n      self.asyncio_loop.run_forever()\n    File \"F:\\anaconda3\\envs\\tf\\lib\\asyncio\\base_events.py\", line 541, in run_forever\n      self._run_once()\n    File \"F:\\anaconda3\\envs\\tf\\lib\\asyncio\\base_events.py\", line 1786, in _run_once\n      handle._run()\n    File \"F:\\anaconda3\\envs\\tf\\lib\\asyncio\\events.py\", line 88, in _run\n      self._context.run(self._callback, *self._args)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 510, in dispatch_queue\n      await self.process_one()\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 499, in process_one\n      await dispatch(*args)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 406, in dispatch_shell\n      await result\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 730, in execute_request\n      reply_content = await reply_content\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 390, in do_execute\n      res = shell.run_cell(code, store_history=store_history, silent=silent)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 528, in run_cell\n      return super().run_cell(*args, **kwargs)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2915, in run_cell\n      raw_cell, store_history, silent, shell_futures)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2960, in _run_cell\n      return runner(coro)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\IPython\\core\\async_helpers.py\", line 78, in _pseudo_sync_runner\n      coro.send(None)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3186, in run_cell_async\n      interactivity=interactivity, compiler=compiler, result=result)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3377, in run_ast_nodes\n      if (await self.run_code(code, result,  async_=asy)):\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3457, in run_code\n      exec(code_obj, self.user_global_ns, self.user_ns)\n    File \"C:\\Users\\THINKPAD\\AppData\\Local\\Temp\\ipykernel_23036\\758426550.py\", line 2, in <module>\n      predictions = model.predict(test_dataset)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 65, in error_handler\n      return fn(*args, **kwargs)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\engine\\training.py\", line 2253, in predict\n      tmp_batch_outputs = self.predict_function(iterator)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\engine\\training.py\", line 2041, in predict_function\n      return step_function(self, iterator)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\engine\\training.py\", line 2027, in step_function\n      outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\engine\\training.py\", line 2015, in run_step\n      outputs = model.predict_step(data)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\engine\\training.py\", line 1983, in predict_step\n      return self(x, training=False)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 65, in error_handler\n      return fn(*args, **kwargs)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\engine\\training.py\", line 557, in __call__\n      return super().__call__(*args, **kwargs)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 65, in error_handler\n      return fn(*args, **kwargs)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\engine\\base_layer.py\", line 1097, in __call__\n      outputs = call_fn(inputs, *args, **kwargs)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 96, in error_handler\n      return fn(*args, **kwargs)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\engine\\functional.py\", line 510, in call\n      return self._run_internal_graph(inputs, training=training, mask=mask)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\engine\\functional.py\", line 649, in _run_internal_graph\n      y = self._conform_to_reference_input(y, ref_input=x)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\engine\\functional.py\", line 761, in _conform_to_reference_input\n      tensor = tf.cast(tensor, dtype=ref_input.dtype)\nNode: 'model_13/Cast_2'\nCast string to float is not supported\n\t [[{{node model_13/Cast_2}}]] [Op:__inference_predict_function_2073971]",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mUnimplementedError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_23036\\758426550.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# print some predict results\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mpredictions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_dataset\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mtrue_num\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mprediction\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgoodRating\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m22440\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_dataset\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m22440\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m#     print(goodRating)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mF:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     68\u001b[0m             \u001b[1;31m# To get the full stack trace, call:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     69\u001b[0m             \u001b[1;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 70\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     71\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     72\u001b[0m             \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mF:\\anaconda3\\envs\\tf\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     53\u001b[0m     \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[1;32m---> 55\u001b[1;33m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[0;32m     56\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mUnimplementedError\u001b[0m: Graph execution error:\n\nDetected at node 'model_13/Cast_2' defined at (most recent call last):\n    File \"F:\\anaconda3\\envs\\tf\\lib\\runpy.py\", line 193, in _run_module_as_main\n      \"__main__\", mod_spec)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\runpy.py\", line 85, in _run_code\n      exec(code, run_globals)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\ipykernel_launcher.py\", line 17, in <module>\n      app.launch_new_instance()\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\traitlets\\config\\application.py\", line 992, in launch_instance\n      app.start()\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 712, in start\n      self.io_loop.start()\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 215, in start\n      self.asyncio_loop.run_forever()\n    File \"F:\\anaconda3\\envs\\tf\\lib\\asyncio\\base_events.py\", line 541, in run_forever\n      self._run_once()\n    File \"F:\\anaconda3\\envs\\tf\\lib\\asyncio\\base_events.py\", line 1786, in _run_once\n      handle._run()\n    File \"F:\\anaconda3\\envs\\tf\\lib\\asyncio\\events.py\", line 88, in _run\n      self._context.run(self._callback, *self._args)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 510, in dispatch_queue\n      await self.process_one()\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 499, in process_one\n      await dispatch(*args)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 406, in dispatch_shell\n      await result\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 730, in execute_request\n      reply_content = await reply_content\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 390, in do_execute\n      res = shell.run_cell(code, store_history=store_history, silent=silent)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 528, in run_cell\n      return super().run_cell(*args, **kwargs)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2915, in run_cell\n      raw_cell, store_history, silent, shell_futures)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2960, in _run_cell\n      return runner(coro)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\IPython\\core\\async_helpers.py\", line 78, in _pseudo_sync_runner\n      coro.send(None)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3186, in run_cell_async\n      interactivity=interactivity, compiler=compiler, result=result)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3377, in run_ast_nodes\n      if (await self.run_code(code, result,  async_=asy)):\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3457, in run_code\n      exec(code_obj, self.user_global_ns, self.user_ns)\n    File \"C:\\Users\\THINKPAD\\AppData\\Local\\Temp\\ipykernel_23036\\758426550.py\", line 2, in <module>\n      predictions = model.predict(test_dataset)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 65, in error_handler\n      return fn(*args, **kwargs)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\engine\\training.py\", line 2253, in predict\n      tmp_batch_outputs = self.predict_function(iterator)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\engine\\training.py\", line 2041, in predict_function\n      return step_function(self, iterator)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\engine\\training.py\", line 2027, in step_function\n      outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\engine\\training.py\", line 2015, in run_step\n      outputs = model.predict_step(data)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\engine\\training.py\", line 1983, in predict_step\n      return self(x, training=False)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 65, in error_handler\n      return fn(*args, **kwargs)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\engine\\training.py\", line 557, in __call__\n      return super().__call__(*args, **kwargs)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 65, in error_handler\n      return fn(*args, **kwargs)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\engine\\base_layer.py\", line 1097, in __call__\n      outputs = call_fn(inputs, *args, **kwargs)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 96, in error_handler\n      return fn(*args, **kwargs)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\engine\\functional.py\", line 510, in call\n      return self._run_internal_graph(inputs, training=training, mask=mask)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\engine\\functional.py\", line 649, in _run_internal_graph\n      y = self._conform_to_reference_input(y, ref_input=x)\n    File \"F:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\engine\\functional.py\", line 761, in _conform_to_reference_input\n      tensor = tf.cast(tensor, dtype=ref_input.dtype)\nNode: 'model_13/Cast_2'\nCast string to float is not supported\n\t [[{{node model_13/Cast_2}}]] [Op:__inference_predict_function_2073971]"
     ]
    }
   ],
   "source": [
    "# print some predict results\n",
    "predictions = model.predict(test_dataset)\n",
    "true_num = 0\n",
    "for prediction, goodRating in zip(predictions[:22440], list(test_dataset)[0][1][:22440]):\n",
    "#     print(goodRating)\n",
    "    if((prediction[0]>0.5) and bool(goodRating)):\n",
    "#         print(prediction,bool(goodRating))\n",
    "        true_num += 1\n",
    "    if((prediction[0]<0.5) and not bool(goodRating)):\n",
    "        true_num += 1\n",
    "#     print(\"Predicted good rating: {:.2%}\".format(prediction[0]),\n",
    "#           \" | Actual rating label: \",\n",
    "#           (\"Good Rating\" if bool(goodRating) else \"Bad Rating\"))\n",
    "print(true_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "8a17e19b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<PrefetchDataset element_spec=(OrderedDict([('movieId', TensorSpec(shape=(None,), dtype=tf.int32, name=None)), ('userId', TensorSpec(shape=(None,), dtype=tf.int32, name=None)), ('rating', TensorSpec(shape=(None,), dtype=tf.float32, name=None)), ('timestamp', TensorSpec(shape=(None,), dtype=tf.int32, name=None)), ('releaseYear', TensorSpec(shape=(None,), dtype=tf.int32, name=None)), ('movieGenre1', TensorSpec(shape=(None,), dtype=tf.string, name=None)), ('movieGenre2', TensorSpec(shape=(None,), dtype=tf.string, name=None)), ('movieGenre3', TensorSpec(shape=(None,), dtype=tf.string, name=None)), ('movieRatingCount', TensorSpec(shape=(None,), dtype=tf.int32, name=None)), ('movieAvgRating', TensorSpec(shape=(None,), dtype=tf.float32, name=None)), ('movieRatingStddev', TensorSpec(shape=(None,), dtype=tf.float32, name=None)), ('userRatedMovie1', TensorSpec(shape=(None,), dtype=tf.int32, name=None)), ('userRatedMovie2', TensorSpec(shape=(None,), dtype=tf.int32, name=None)), ('userRatedMovie3', TensorSpec(shape=(None,), dtype=tf.int32, name=None)), ('userRatedMovie4', TensorSpec(shape=(None,), dtype=tf.int32, name=None)), ('userRatedMovie5', TensorSpec(shape=(None,), dtype=tf.int32, name=None)), ('userRatingCount', TensorSpec(shape=(None,), dtype=tf.int32, name=None)), ('userAvgReleaseYear', TensorSpec(shape=(None,), dtype=tf.int32, name=None)), ('userReleaseYearStddev', TensorSpec(shape=(None,), dtype=tf.float32, name=None)), ('userAvgRating', TensorSpec(shape=(None,), dtype=tf.float32, name=None)), ('userRatingStddev', TensorSpec(shape=(None,), dtype=tf.float32, name=None)), ('userGenre1', TensorSpec(shape=(None,), dtype=tf.string, name=None)), ('userGenre2', TensorSpec(shape=(None,), dtype=tf.string, name=None)), ('userGenre3', TensorSpec(shape=(None,), dtype=tf.string, name=None)), ('userGenre4', TensorSpec(shape=(None,), dtype=tf.string, name=None)), ('userGenre5', TensorSpec(shape=(None,), dtype=tf.string, name=None))]), TensorSpec(shape=(None,), dtype=tf.int32, name=None))>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e55af666",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "bcc304cf",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"dot\" with args ['-Tpng', 'C:\\\\Users\\\\THINKPAD\\\\AppData\\\\Local\\\\Temp\\\\tmpig470ulj'] returned code: 1\n",
      "\n",
      "stdout, stderr:\n",
      " b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIHDR\\x00\\x00\\x07$\\x00\\x00\\x02e\\x08\\x02\\x00\\x00\\x00\\x14\\xc2\\xd2\\xac\\x00\\x00\\x00\\x06bKGD\\x00\\xff\\x00\\xff\\x00\\xff\\xa0\\xbd\\xa7\\x93\\x00\\x00 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      "b\"Error: bad label format {item|DenseFeatures}|{input:|output:}|{{/{'releaseYear': (None, 1), 'movieId': (None,)/}}|{(None, 11)}}\\r\\nError: bad label format {user|DenseFeatures}|{input:|output:}|{{/{'userId': (None,), 'userAvgRating': (None,), 'userRatingStddev': (None,), 'userGenre1': (None,)/}}|{(None, 22)}}\\r\\n\"\n",
      "\n"
     ]
    },
    {
     "ename": "AssertionError",
     "evalue": "\"dot\" with args ['-Tpng', 'C:\\\\Users\\\\THINKPAD\\\\AppData\\\\Local\\\\Temp\\\\tmpig470ulj'] returned code: 1",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_3596\\2973098902.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mplot_model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;31m# 绘制网络结构图\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mplot_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mto_file\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'NeuralCF.png'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mshow_shapes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mF:\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\utils\\vis_utils.py\u001b[0m in \u001b[0;36mplot_model\u001b[1;34m(model, to_file, show_shapes, show_dtype, show_layer_names, rankdir, expand_nested, dpi, layer_range, show_layer_activations)\u001b[0m\n\u001b[0;32m    469\u001b[0m         \u001b[0mextension\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mextension\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    470\u001b[0m     \u001b[1;31m# Save image to disk.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 471\u001b[1;33m     \u001b[0mdot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mto_file\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mextension\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    472\u001b[0m     \u001b[1;31m# Return the image as a Jupyter Image object, to be displayed in-line.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    473\u001b[0m     \u001b[1;31m# Note that we cannot easily detect whether the code is running in a\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mF:\\anaconda3\\envs\\tf\\lib\\site-packages\\pydot.py\u001b[0m in \u001b[0;36mwrite\u001b[1;34m(self, path, prog, format, encoding)\u001b[0m\n\u001b[0;32m   1826\u001b[0m                 \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1827\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1828\u001b[1;33m             \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1829\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'wb'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1830\u001b[0m                 \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mF:\\anaconda3\\envs\\tf\\lib\\site-packages\\pydot.py\u001b[0m in \u001b[0;36mcreate\u001b[1;34m(self, prog, format, encoding)\u001b[0m\n\u001b[0;32m   1958\u001b[0m                     \u001b[0mprog\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mprog\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1959\u001b[0m                     \u001b[0marguments\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0marguments\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1960\u001b[1;33m                     \u001b[0mcode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mprocess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreturncode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1961\u001b[0m                 )\n\u001b[0;32m   1962\u001b[0m             )\n",
      "\u001b[1;31mAssertionError\u001b[0m: \"dot\" with args ['-Tpng', 'C:\\\\Users\\\\THINKPAD\\\\AppData\\\\Local\\\\Temp\\\\tmpig470ulj'] returned code: 1"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.utils import plot_model\n",
    "# 绘制网络结构图\n",
    "plot_model(model,to_file='NeuralCF.png',show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32f3ae7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "359a5325",
   "metadata": {},
   "outputs": [],
   "source": [
    "for features,label in test_dataset.take(10):\n",
    "    print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2377d8fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70c0b20f",
   "metadata": {},
   "source": [
    "### 模型保存"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "60ffa757",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Function `_wrapped_model` contains input name(s) movieId, releaseYear, userAvgRating, userGenre1, userId, userRatingStddev with unsupported characters which will be renamed to movieid, releaseyear, useravgrating, usergenre1, userid, userratingstddev in the SavedModel.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: modelParameter\\NeuralCF\\005\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: modelParameter\\NeuralCF\\005\\assets\n"
     ]
    }
   ],
   "source": [
    "tf.keras.models.save_model(\n",
    "    model,\n",
    "    r\"modelParameter\\NeuralCF\\005\",\n",
    "    overwrite=True,\n",
    "    include_optimizer=True,\n",
    "    save_format=None,\n",
    "    signatures=None,\n",
    "    options=None\n",
    ")"
   ]
  }
 ],
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